Learning Biped Locomotion from First Principles on a Simulated Humanoid Robot Using Linear Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{wolff:2003:gecco,
  author =       "Krister Wolff and Peter Nordin",
  title =        "Learning Biped Locomotion from First Principles on a
                 Simulated Humanoid Robot Using Linear Genetic
                 Programming",
  booktitle =    "Genetic and Evolutionary Computation -- GECCO-2003",
  editor =       "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and 
                 D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and 
                 R. Standish and G. Kendall and S. Wilson and 
                 M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and 
                 A. C. Schultz and K. Dowsland and N. Jonoska and 
                 J. Miller",
  year =         "2003",
  pages =        "495--506",
  address =      "Chicago",
  publisher_address = "Berlin",
  month =        "12-16 " # jul,
  volume =       "2723",
  series =       "LNCS",
  ISBN =         "3-540-40602-6",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 Robotics",
  URL =          "http://fy.chalmers.se/~wolff/WN_gecco03.pdf",
  DOI =          "doi:10.1007/3-540-45105-6_61",
  abstract =     "We describe the first instance of an approach for
                 control programming of humanoid robots, based on
                 evolution as the main adaptation mechanism. In an
                 attempt to overcome some of the difficulties with
                 evolution on real hardware, we use a physically
                 realistic simulation of the robot. The essential idea
                 in this concept is to evolve control programs from
                 first principles on a simulated robot, transfer the
                 resulting programs to the real robot and continue to
                 evolve on the robot. The Genetic Programming system is
                 implemented as a Virtual Register Machine, with 12
                 internal work registers and 12 external registers for
                 I/O operations. The individual representation scheme is
                 a linear genome, and the selection method is a steady
                 state tournament algorithm. Evolution created
                 controller programs that made the simulated robot
                 produce forward locomotion behavior. An application of
                 this system with two phases of evolution could be for
                 robots working in hazardous environments, or in
                 applications with remote presence robots.",
  notes =        "GECCO-2003. A joint meeting of the twelfth
                 International Conference on Genetic Algorithms
                 (ICGA-2003) and the eighth Annual Genetic Programming
                 Conference (GP-2003)",
}

Genetic Programming entries for Krister Wolff Peter Nordin

Citations